Governance at the Edge of Architecture: Regulating NeuroAI and Neuromorphic Systems

TL;DR

This paper discusses the challenges of applying traditional AI governance frameworks to NeuroAI and neuromorphic systems, emphasizing the need for new assurance methods aligned with their unique physics and dynamics.

Contribution

It highlights the limitations of current AI governance standards for NeuroAI and proposes the evolution of assurance and audit methods tailored to neuromorphic architectures.

Findings

Current benchmarks are inadequate for NeuroAI systems

NeuroAI requires regulation aligned with brain-inspired physics

Traditional assurance methods need adaptation for neuromorphic hardware

Abstract

Current AI governance frameworks, including regulatory benchmarks for accuracy, latency, and energy efficiency, are built for static, centrally trained artificial neural networks on von Neumann hardware. NeuroAI systems, embodied in neuromorphic hardware and implemented via spiking neural networks, break these assumptions. This paper examines the limitations of current AI governance frameworks for NeuroAI, arguing that assurance and audit methods must co-evolve with these architectures, aligning traditional regulatory metrics with the physics, learning dynamics, and embodied efficiency of brain-inspired computation to enable technically grounded assurance.